Assignment 8: Visualization - Making a bar race

How to do it?:

• Open the Rmarkdown file of this assignment (link) in Rstudio.

• Right under each question, insert a code chunk (you can use the hotkey Ctrl + Alt + I to add a code chunk) and code the solution for the question.

• Knit the rmarkdown file (hotkey: Ctrl + Alt + K) to export an html.

• Publish the html file to your Githiub Page.

Submission: Submit the link on Github of the assignment to Canvas

---

1. Install two packages gganimate and gifski then restart Rstudio. Using the Adult Census Income data, make an animation using geom_point and transition_states.

library(ggplot2)
library(gganimate)
library(gifski)
library(tidyverse)
library(knitr)
library(lubridate)

df <- read.csv('https://bryantstats.github.io/math421/data/adult_census.csv')
df <- as.tibble(df)

## Warning: `as.tibble()` was deprecated in tibble 2.0.0.
## ℹ Please use `as_tibble()` instead.
## ℹ The signature and semantics have changed, see `?as_tibble`.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

df %>% 
  filter(native.country == "Canada") %>% 
  ggplot(aes(x= hours.per.week, y = age, color= income))+
    geom_point()+
    transition_states(sex)+
    labs(title = 'Sex: {closest_state}')

2. Using the Adult Census Income data, make an animation using geom_bar and transition_states.

df %>% 
  filter(native.country == "Canada") %>% 
  ggplot(aes(x= income,fill= sex))+
    geom_bar(position = 'fill')+
    transition_states(race)+
    labs(title = 'Income: {closest_state}')

3. Use the WHO’s dataset at this link. Make a top-10 bar race by months between countries on the number of deaths by Covid 19 in 2021.

library(tweenr)
df1 <- read.csv('WHO-COVID-19-global-data.csv')
df1 <- as_tibble(df1)
df1$Date_reported <- as.Date(df1$Date_reported)
df1$year <- year(df1$Date_reported)
df1$month <- month(df1$Date_reported)

d1 <- df1 %>% 
  filter(year == 2021)%>% 
  group_by(year, Country) %>% 
  summarise(sum = sum(Cumulative_deaths)) 

d2 <- d1 %>% 
  group_by(year) %>% 
  mutate(rank=rank(-sum))

d1 <- df1 %>% 
  group_by(year, Country) %>% 
  summarise(mean = mean(Cumulative_deaths))
d2 <- d1 %>% group_by(year) %>% mutate(rank=rank(-mean)) 
d3 <- d2 %>% filter(rank <= 10)
a1 <- d3 %>% ggplot(aes(x=rank, y=mean, group=Country, fill=Country, label=Country)) + geom_col()+
    geom_text(aes(y = mean, label = Country), hjust = 1.4)+ 
    coord_flip(clip = "off", expand = FALSE) +scale_x_reverse()+
    labs(title = 'Year {closest_state}', x='', y='Total Number of Positive Caeses', fill='Country')+
    theme(plot.title = element_text(hjust = 1, size = 22),
          axis.ticks.y = element_blank(),
          axis.text.y  = element_blank()) + 
    transition_states(year)+
    ease_aes("cubic-in-out")
animate(a1, nframes = 400)

4. Make a bar race using a dataset of your own interest. You may use the dataset that we use in class (https://covidtracking.com/data/download/all-states-history.csv) but you should make a different bar racev from ones in the slides.

df2 <- read.csv("all-states-history.csv")
df2 <- as_tibble(df2)
df2$date <- as.Date(df2$date)
df2$month <- month(df2$date)

d1 <- df2 %>% group_by(month, state) %>% summarise(sum = sum(death))
d2 <- d1 %>% group_by(month) %>% mutate(rank=rank(-sum)) 
d3 <- d2 %>% filter(rank <= 10)

a1 <- d3 %>% ggplot(aes(x=rank, y=sum, group=state, fill=state, label=state)) +  geom_col()+
    geom_text(aes(y = sum, label = state), hjust = 1.4)+ 
    coord_flip(clip = "off", expand = FALSE) +scale_x_reverse()+
    labs(title = 'Month {closest_state}', x='', y='Total Number of Positive Caeses', fill= 'state')+
    theme(plot.title = element_text(hjust = 1, size = 22),
          axis.ticks.y = element_blank(),
          axis.text.y  = element_blank()) + 
    transition_states(month)+
    ease_aes("cubic-in-out")
animate(a1, nframes = 400)